Update LICENSE to AGPL-3.0 (#2031)

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Glenn Jocher 2 years ago committed by GitHub
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      LICENSE
  13. 4
      README.md
  14. 4
      README.zh-CN.md
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      docker/Dockerfile
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      docs/yolov5/hyp_evolution.md
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      mkdocs.yml
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      setup.py
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      tests/test_cli.py
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      tests/test_engine.py
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      tests/test_python.py
  24. 2
      ultralytics/__init__.py
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      ultralytics/datasets/Argoverse.yaml
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      ultralytics/datasets/GlobalWheat2020.yaml
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  40. 2
      ultralytics/hub/__init__.py
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      ultralytics/models/v3/yolov3-spp.yaml
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      ultralytics/models/v3/yolov3.yaml
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      ultralytics/models/v5/yolov5-p6.yaml
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      ultralytics/models/v5/yolov5.yaml
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      ultralytics/models/v8/yolov8-cls.yaml
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      ultralytics/models/v8/yolov8-p2.yaml
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      ultralytics/tracker/utils/kalman_filter.py
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      ultralytics/yolo/data/scripts/download_weights.sh
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      ultralytics/yolo/data/scripts/get_coco.sh
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      ultralytics/yolo/data/scripts/get_imagenet.sh
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      ultralytics/yolo/engine/model.py
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      ultralytics/yolo/engine/validator.py
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      ultralytics/yolo/utils/__init__.py
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      ultralytics/yolo/utils/autobatch.py
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      ultralytics/yolo/utils/benchmarks.py
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@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# README translation action to translate README.md to Chinese as README.zh-CN.md on any change to README.md
name: Translate README

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO Continuous Integration (CI) GitHub Actions tests
name: Ultralytics CI

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
name: CLA Assistant
on:

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Builds ultralytics/ultralytics:latest images on DockerHub https://hub.docker.com/r/ultralytics
name: Publish Docker Images

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
name: Greetings

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO Continuous Integration (CI) GitHub Actions tests
name: Check Broken links

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Publish pip package to PyPI https://pypi.org/project/ultralytics/ and Docs to https://docs.ultralytics.com
name: Publish to PyPI and Deploy Docs

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
name: Close stale issues
on:

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Pre-commit hooks. For more information see https://github.com/pre-commit/pre-commit-hooks/blob/main/README.md
exclude: 'docs/'

@ -16,5 +16,5 @@ preferred-citation:
version: 8.0.0
# doi: 10.5281/zenodo.3908559 # TODO
date-released: 2023-1-10
license: GPL-3.0
license: AGPL-3.0
url: "https://github.com/ultralytics/ultralytics"

@ -112,4 +112,4 @@ understand and diagnose your problem.
## License
By contributing, you agree that your contributions will be licensed under
the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/)
the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/)

@ -1,5 +1,5 @@
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
@ -7,17 +7,15 @@
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The GNU Affero General Public License is a free, copyleft license for
software and other kinds of works, specifically designed to ensure
cooperation with the community in the case of network server software.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
our General Public Licenses are intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
software for all its users.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
@ -26,44 +24,34 @@ them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
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Some devices are designed to deny users access to install or run
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Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
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avoid the special danger that patents applied to a free program could
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patents cannot be used to render the program non-free.
Developers that use our General Public Licenses protect your rights
with two steps: (1) assert copyright on the software, and (2) offer
you this License which gives you legal permission to copy, distribute
and/or modify the software.
A secondary benefit of defending all users' freedom is that
improvements made in alternate versions of the program, if they
receive widespread use, become available for other developers to
incorporate. Many developers of free software are heartened and
encouraged by the resulting cooperation. However, in the case of
software used on network servers, this result may fail to come about.
The GNU General Public License permits making a modified version and
letting the public access it on a server without ever releasing its
source code to the public.
The GNU Affero General Public License is designed specifically to
ensure that, in such cases, the modified source code becomes available
to the community. It requires the operator of a network server to
provide the source code of the modified version running there to the
users of that server. Therefore, public use of a modified version, on
a publicly accessible server, gives the public access to the source
code of the modified version.
An older license, called the Affero General Public License and
published by Affero, was designed to accomplish similar goals. This is
a different license, not a version of the Affero GPL, but Affero has
released a new version of the Affero GPL which permits relicensing under
this license.
The precise terms and conditions for copying, distribution and
modification follow.
@ -72,7 +60,7 @@ modification follow.
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"This License" refers to version 3 of the GNU Affero General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
@ -549,35 +537,45 @@ to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
13. Remote Network Interaction; Use with the GNU General Public License.
Notwithstanding any other provision of this License, if you modify the
Program, your modified version must prominently offer all users
interacting with it remotely through a computer network (if your version
supports such interaction) an opportunity to receive the Corresponding
Source of your version by providing access to the Corresponding Source
from a network server at no charge, through some standard or customary
means of facilitating copying of software. This Corresponding Source
shall include the Corresponding Source for any work covered by version 3
of the GNU General Public License that is incorporated pursuant to the
following paragraph.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
under version 3 of the GNU General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
but the work with which it is combined will remain governed by version
3 of the GNU General Public License.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
the GNU Affero General Public License from time to time. Such new versions
will be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Program specifies that a certain numbered version of the GNU Affero General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
GNU Affero General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
versions of the GNU Affero General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
@ -635,40 +633,29 @@ the "copyright" line and a pointer to where the full notice is found.
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
GNU Affero General Public License for more details.
You should have received a copy of the GNU General Public License
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

@ -225,8 +225,8 @@ We love your input! YOLOv5 and YOLOv8 would not be possible without help from ou
YOLOv8 is available under two different licenses:
- **GPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
- **AGPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of AGPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
## <div align="center">Contact</div>

@ -224,8 +224,8 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
YOLOv8 提供两种不同的许可证:
- **GPL-3.0 许可证**:详细信息请参阅 [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件。
- **企业许可证**:为商业产品开发提供更大的灵活性,无需遵循 GPL-3.0 的开源要求。典型的用例是将 Ultralytics 软件和 AI 模型嵌入商业产品和应用中。在 [Ultralytics 授权](https://ultralytics.com/license) 处申请企业许可证。
- **AGPL-3.0 许可证**:详细信息请参阅 [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件。
- **企业许可证**:为商业产品开发提供更大的灵活性,无需遵循 AGPL-3.0 的开源要求。典型的用例是将 Ultralytics 软件和 AI 模型嵌入商业产品和应用中。在 [Ultralytics 授权](https://ultralytics.com/license) 处申请企业许可证。
## <div align="center">联系方式</div>

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
@ -40,12 +40,12 @@ ENV DEBIAN_FRONTEND teletype
# Usage Examples -------------------------------------------------------------------------------------------------------
docker pull ultralytics/ultralytics:latest-hub-export-edgetpu-paddle
# Build and Push
# t=ultralytics/ultralytics:latest && sudo docker build -f docker/Dockerfile -t $t . && sudo docker push $t
# Pull and Run
# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
# t=ultralytics/ultralytics/ultralytics:latest-hub-export-edgetpu-paddle && sudo docker pull $t && sudo docker run -it --ipc=host $t
# Pull and Run with local directory access
# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Builds ultralytics/ultralytics:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments

@ -19,7 +19,7 @@ pip install -r requirements.txt # install
YOLOv5 has about 30 hyperparameters used for various training settings. These are defined in `*.yaml` files in the `/data/hyps` directory. Better initial guesses will produce better final results, so it is important to initialize these values properly before evolving. If in doubt, simply use the default values, which are optimized for YOLOv5 COCO training from scratch.
```yaml
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Hyperparameters for low-augmentation COCO training from scratch
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
site_name: YOLOv8 Docs
repo_url: https://github.com/ultralytics/ultralytics

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import re
from pathlib import Path
@ -23,7 +23,7 @@ setup(
name='ultralytics', # name of pypi package
version=get_version(), # version of pypi package
python_requires='>=3.7',
license='GPL-3.0',
license='AGPL-3.0',
description='Ultralytics YOLOv8',
long_description=README,
long_description_content_type='text/markdown',

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import subprocess
from pathlib import Path

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from pathlib import Path

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from pathlib import Path

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.77'

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
# Example usage: yolo train data=Argoverse.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
# Example usage: yolo train data=GlobalWheat2020.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
# Example usage: yolo train task=classify data=imagenet

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Objects365 dataset https://www.objects365.org/ by Megvii
# Example usage: yolo train data=Objects365.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
# Example usage: yolo train data=SKU-110K.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
# Example usage: yolo train data=VOC.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
# Example usage: yolo train data=VisDrone.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO 2017 dataset http://cocodataset.org by Microsoft
# Example usage: yolo train data=coco-pose.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO 2017 dataset http://cocodataset.org by Microsoft
# Example usage: yolo train data=coco.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: yolo train data=coco128.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: yolo train data=coco128.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics
# Example usage: yolo train data=coco8-pose.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
# Example usage: yolo train data=coco8-seg.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
# Example usage: yolo train data=coco8.yaml
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
# Example usage: yolo train data=xView.yaml

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import requests

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import requests

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import signal
import sys
from pathlib import Path

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import os
import platform

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
# Parameters

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8-pose keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
# Parameters

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8-seg instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
# Parameters

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import ast
import contextlib
import json

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Common modules
"""

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Common modules
"""

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
from copy import deepcopy

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from .track import register_tracker
from .trackers import BOTSORT, BYTETracker

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Default YOLO tracker settings for BoT-SORT tracker https://github.com/NirAharon/BoT-SORT
tracker_type: botsort # tracker type, ['botsort', 'bytetrack']

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Default YOLO tracker settings for ByteTrack tracker https://github.com/ifzhang/ByteTrack
tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from functools import partial

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from .bot_sort import BOTSORT
from .byte_tracker import BYTETracker

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from collections import OrderedDict

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from collections import deque

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import numpy as np

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import copy

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import numpy as np
import scipy.linalg

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import numpy as np
import scipy

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from . import v8

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
import re
import shutil

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training
task: detect # YOLO task, i.e. detect, segment, classify, pose

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from .base import BaseDataset
from .build import build_classification_dataloader, build_dataloader, load_inference_source

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import math
import random

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import glob
import math

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import os
import random

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import glob
import math

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Image augmentation functions
"""

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Dataloaders and dataset utils
"""

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
from itertools import repeat
from multiprocessing.pool import ThreadPool

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import collections
from copy import deepcopy

@ -1,5 +1,5 @@
#!/bin/bash
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Download latest models from https://github.com/ultralytics/assets/releases
# Example usage: bash ultralytics/yolo/data/scripts/download_weights.sh
# parent

@ -1,5 +1,5 @@
#!/bin/bash
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Download COCO 2017 dataset http://cocodataset.org
# Example usage: bash data/scripts/get_coco.sh
# parent

@ -1,5 +1,5 @@
#!/bin/bash
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
# Example usage: bash data/scripts/get_coco128.sh
# parent

@ -1,5 +1,5 @@
#!/bin/bash
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Download ILSVRC2012 ImageNet dataset https://image-net.org
# Example usage: bash data/scripts/get_imagenet.sh
# parent

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
import hashlib

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Export a YOLOv8 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
@ -213,7 +213,7 @@ class Exporter:
self.metadata = {
'description': description,
'author': 'Ultralytics',
'license': 'GPL-3.0 https://ultralytics.com/license',
'license': 'AGPL-3.0 https://ultralytics.com/license',
'version': __version__,
'stride': int(max(model.stride)),
'task': model.task,

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import sys
from pathlib import Path

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Ultralytics Results, Boxes and Masks classes for handling inference results

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Train a model on a dataset

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Check a model's accuracy on a test or val split of a dataset

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
import inspect

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Functions for estimating the best YOLO batch size to use a fraction of the available CUDA memory in PyTorch.
"""

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Benchmark a YOLO model formats for speed and accuracy

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Base callbacks
"""

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import re
import matplotlib.image as mpimg

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import os
from pathlib import Path

@ -1,4 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Ultralytics YOLO 🚀, AGPL-3.0 license
import json
from time import time

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